How to improve the time series predictions using Random Forest?
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We are running RandomForest model on a time-series data. The model is run in real time and is refit every time a new row is added. Since it is a timeseries data, we set shuffle to false while splitting into train and test dataset.
We observed that there is a drastic change in scores when shuffle is True and when shuffle is false
The code being used is as follows
# Set shuffle = 'True' or 'False'
df = pandas.read_csv('data.csv', index_col=0)
X = df.drop(columns=['label'])
y = df['label']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05, shuffle=True)
count = 0
predictions =
for idx in X_test.index.values:
# Train the model on training data
# print(count, X_train.shape, y_train.shape)
rf = RandomForestRegressor(n_estimators = 600, max_depth = 7, random_state = 12345)
rf.fit(X_train, y_train)
predictions.append(rf.predict(X_test.loc[X_test.index == idx]))
# print(len(predictions))
X_train.loc[len(X_train)] = X_test.loc[idx]
y_train.loc[len(y_train)] = y_test.loc[idx]
count+=1
Initially, we thought the difference is due to covariance shift in the data. But that shouldn't affect this much for continuous fit
Here are screenshots for the prediction plot
With shuffle = False
With shuffle = True
time-series predictive-modeling random-forest training transfer-learning
asked 4 mins ago
Sumesh SurendranSumesh Surendran
1
New contributor
Sumesh Surendran is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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$begingroup$
We are running RandomForest model on a time-series data. The model is run in real time and is refit every time a new row is added. Since it is a timeseries data, we set shuffle to false while splitting into train and test dataset.
We observed that there is a drastic change in scores when shuffle is True and when shuffle is false
The code being used is as follows
# Set shuffle = 'True' or 'False'
df = pandas.read_csv('data.csv', index_col=0)
X = df.drop(columns=['label'])
y = df['label']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05, shuffle=True)
count = 0
predictions =
for idx in X_test.index.values:
# Train the model on training data
# print(count, X_train.shape, y_train.shape)
rf = RandomForestRegressor(n_estimators = 600, max_depth = 7, random_state = 12345)
rf.fit(X_train, y_train)
predictions.append(rf.predict(X_test.loc[X_test.index == idx]))
# print(len(predictions))
X_train.loc[len(X_train)] = X_test.loc[idx]
y_train.loc[len(y_train)] = y_test.loc[idx]
count+=1
Initially, we thought the difference is due to covariance shift in the data. But that shouldn't affect this much for continuous fit
Here are screenshots for the prediction plot
With shuffle = False
With shuffle = True
time-series predictive-modeling random-forest training transfer-learning
asked 4 mins ago
Sumesh SurendranSumesh Surendran
1
New contributor
Sumesh Surendran is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
We are running RandomForest model on a time-series data. The model is run in real time and is refit every time a new row is added. Since it is a timeseries data, we set shuffle to false while splitting into train and test dataset.
We observed that there is a drastic change in scores when shuffle is True and when shuffle is false
The code being used is as follows
# Set shuffle = 'True' or 'False'
df = pandas.read_csv('data.csv', index_col=0)
X = df.drop(columns=['label'])
y = df['label']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05, shuffle=True)
count = 0
predictions =
for idx in X_test.index.values:
# Train the model on training data
# print(count, X_train.shape, y_train.shape)
rf = RandomForestRegressor(n_estimators = 600, max_depth = 7, random_state = 12345)
rf.fit(X_train, y_train)
predictions.append(rf.predict(X_test.loc[X_test.index == idx]))
# print(len(predictions))
X_train.loc[len(X_train)] = X_test.loc[idx]
y_train.loc[len(y_train)] = y_test.loc[idx]
count+=1
Initially, we thought the difference is due to covariance shift in the data. But that shouldn't affect this much for continuous fit
Here are screenshots for the prediction plot
With shuffle = False
With shuffle = True
time-series predictive-modeling random-forest training transfer-learning
asked 4 mins ago
Sumesh SurendranSumesh Surendran
1
New contributor
Sumesh Surendran is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
We are running RandomForest model on a time-series data. The model is run in real time and is refit every time a new row is added. Since it is a timeseries data, we set shuffle to false while splitting into train and test dataset.
We observed that there is a drastic change in scores when shuffle is True and when shuffle is false
The code being used is as follows
# Set shuffle = 'True' or 'False'
df = pandas.read_csv('data.csv', index_col=0)
X = df.drop(columns=['label'])
y = df['label']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05, shuffle=True)
count = 0
predictions =
for idx in X_test.index.values:
# Train the model on training data
# print(count, X_train.shape, y_train.shape)
rf = RandomForestRegressor(n_estimators = 600, max_depth = 7, random_state = 12345)
rf.fit(X_train, y_train)
predictions.append(rf.predict(X_test.loc[X_test.index == idx]))
# print(len(predictions))
X_train.loc[len(X_train)] = X_test.loc[idx]
y_train.loc[len(y_train)] = y_test.loc[idx]
count+=1
Initially, we thought the difference is due to covariance shift in the data. But that shouldn't affect this much for continuous fit
Here are screenshots for the prediction plot
With shuffle = False
With shuffle = True
time-series predictive-modeling random-forest training transfer-learning
time-series predictive-modeling random-forest training transfer-learning
asked 4 mins ago
Sumesh SurendranSumesh Surendran
1
New contributor
Sumesh Surendran is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 4 mins ago
Sumesh SurendranSumesh Surendran
1
New contributor
Sumesh Surendran is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 4 mins ago
Sumesh SurendranSumesh Surendran
1
New contributor
Sumesh Surendran is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 4 mins ago
Sumesh SurendranSumesh Surendran
1
asked 4 mins ago
Sumesh SurendranSumesh Surendran
1
1
New contributor
Sumesh Surendran is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Sumesh Surendran is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Sumesh Surendran is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
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